| 課程目標 |
以計算方法介紹資訊科技於會計、管理及研究方法上的應用。主要討論數學規劃及資料包絡分析(Data Envelopment Analysis)，演化運算及倒傳遞(BP)及自我組織(SOM)類神經網路。 |
Objectives |
This course introduces the information technology applications for accounting, management and research by computational methods. It also focuses on the mathematical programming, DEA(Data envelopment analysis), evolutionary and back propagation(BP) and self organizing maps(SOM) artificial neural networks. |
| 教材 |
Tim Coelli and etc., An Introduction to Efficiency and Productivity Analysis, Kluwer, 1998
Vanderbei, Robert J., Linear Programming: Foundations and Extensions
W.N. Venables and B.D. Ripley, Modern Applied Statistics with S, 2002, Spring
Trevor Hastie, Robert Tibshirani and Jerome Fredman, The Elements of Statistical Learning, 2001, Springer
R. J. Roiger and M. W. Geatz, Data Mining - A tutorial-based primer, Addison Wesley 2003, 東華書局譯
M. Kantardzic, Data Mining - Concepts, models, methods, and algorithms, John Wiley & Sons, 2003
V. Kecman, Learning and Soft computing, The MIT Press, 2001 |
Teaching Materials |
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| 教學內容 |
1.生產效率簡介，R 軟體簡介 (Coelli, Ch01)
2.生產經濟 (Coelli, Ch02, Ch03)
3.生產力測量指標 (Coelli, Ch03, Ch04)
4.線性規劃–Simplex方法 (Vanderbei, Ch01, Ch02, Ch03)
5.線性規劃–對偶性及敏感度 (Vanderbei, Ch05, Ch07, Ch09)
6.DEA效率分析 (Coelli, Ch06)
7.DEA效率分析進階 (Coelli, Ch07)
8.機率前緣效率分析 (Coelli, Ch08)
9.期中考
10.機率前緣效率分析進階 (Coelli, Ch09)
11.通用線性模型 (Venable, Ch07)
12.非線性及平滑回歸 (Venable, Ch08)
13.非線性及平滑回歸(2) (Venable, Ch08)
14.樹基方法(Venable, Ch09)
15.多變量資料探索(Venable, Ch10)
16.分類 (Venable, Ch11)
17.資料探勘應用(Torgo, Case 1)
18.期末考 |
Syllabus |
1.Introduction of efficiency and productivity analysis and R environment. (Coelli, Ch01)
2.Production economics (Coelli, Ch02, Ch03)
3.Index numbers and productivity measurement (Coelli, Ch03, Ch04)
4.Linear programming – Simplex method (Vanderbei, Ch01, Ch02, Ch03)
5.Linear programming – Duality and sensitivity (Vanderbei, Ch05, Ch07, Ch09)
6.Efficiency measurement using data envelopment analysis (Coelli, Ch06)
7.Additional topics on DEA (Coelli, Ch07)
8.Efficiency measurement using stochastic frontier (Coelli, Ch08)
9.Mid term Exam
10.Additional topics on stochastic frontiers (Coelli, Ch09)
11.General linear models (Venable, Ch07)
12.Non-linear and smooth regression (Venable, Ch08)
13.Non-linear and smooth regression (Venable, Ch08)
14.Tree-Based methods (Venable, Ch09)
15.Exploratory multivariate analysis (Venable, Ch10)
16.Classification (Venable, Ch11)
17.Data mining application (Torgo, Case 1)
18.Final Exam. |